The invention relates to adaptive equalizers such as are used in voiceband data sets and other data transmission applications.
Adaptive equalizers for data transmission are typically started up in a so-called ideal reference mode in which a stream of predetermined "ideal reference" values is transmitted to the equalizer over the channel being equalized. The ideal reference data is known a priori at the equalizer and the differences between the equalizer output values, on the one hand, and the known transmitted values, on the other hand, are used by the equalizer as error signals to update its tap coefficients. The latter define the equalizer transfer characteristic, hereinafter also referred to as the equalizer "state". The updating algorithm updates the tap coefficients in such a way as to minimize some function of the error signal--typically its mean-squared value over time. When the equalizer has "converged" to a point at which the mean-squared error is at an absolute, or global, minimum, the equalizer output constellation, i.e., the ensemble of possible equalizer output values, will be substantially congruent with the transmitted constellation, i.e., the ensemble of possible transmitted data symbol values. The channel is then said to be "equalized."
Thereafter, the equalizer operates in response to so-called decision-directed errors in which quantized versions of the equalizer outputs are used in the place of the ideal reference data. The tap coefficients, and thus the equalizer state, are thus continually adapted over time as equalizer operation continues. Advantageously, this allows the equalizer to continually fine-tune its transfer characteristic and thereby compensate, for example, for time-varying effects, such as changes in the communication channel characteristics.
Phenomena such as phase hits and channel switching can result in a subsequent loss of equalization, meaning that the tap coefficients then stored in the equalizer no longer equalize the channel. A new set of tap coefficients which will equalize the channel must then somehow be arrived at, that process being referred to as "re-training." Depending on the level of distortion in the channel, it may be possible to continue to allow the equalizer to simply continue to adapt in a decision-directed mode, starting, for example, with the coefficient values then stored in the equalizer or with some predetermined set of initial values. Disadvantageously, however, it is possible with this approach for the equalizer to converge to an incorrect state in which the decision-directed error function is at a local minimum, rather than being at the above-mentioned absolute, or global, minimum. This results from the fact that, in some equalizer states, the actual and decision-directed errors are different for particular equalizer outputs that represent particular transmitted symbols. The equalizer is thus "stuck" in a stable state in which its output constellation is different from the transmitted constellation and the transmitted data is not correctly recovered.
To avoid this problem, the conventional approach is for a data set whose equalizer needs to be retrained--hereinafter referred to as the "downstream" data set--to transmit a message to the data set at the other end of the channel--hereinafter referred to as the "upstream" data set--requesting the retrain. The ideal reference data is then retransmitted and the equalizer in the downstream data set reconverges to the correct state.
Although generally satisfactory in many applications, this approach has drawbacks. For example, the fact that the downstream data set must communicate its need to be retrained to the upstream data set means that communication of user data from the downstream to the upstream data set, which might well otherwise be able to continue, will have to be interrupted. Moreover, the upstream data set will require some form of detection circuitry to recognize the retrain request, thereby adding to the cost and complexity of the data set.
An additional disadvantage occurs in multipoint networks. In such applications, the need for, say, the (upstream) control data set to transmit ideal reference data over the network for the benefit of a particular (downstream) tributary data set whose equalizer needs to be retrained means that normal communication between the control data set and the other tributary data sets in the network will be interrupted.
It is thus desirable to have a scheme which allows an adaptive equalizer to be trained in a decision-directed mode while eliminating the possibility that it will converge to an incorrect state.